ArticleNature communications2021
Dynamic memory to alleviate catastrophic forgetting in continual learning with medical imaging.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- Keeping Medical AI Healthy and Trustworthy: A Review of Detection and Correction Methods for System Degradation.IEEE transactions on bio-medical engineering · 2026Review
- Artificial intelligence and transforming cancer care.Discover oncology · 2026Review
- From classical machine learning to emerging foundation models: review on multimodal data integration for cancer research.Artificial intelligence review · 2026Article
- SynSAM: a hybrid synchronous learning framework with knowledge retention for prostate zonal segmentation leveraging the segment anything model.Medical & biological engineering & computing · 2026Article
- Development and validation of deep learning for predicting the growth of ovarian cancer organoids.Chinese medical journal · 2026Article
- Evaluation of ChatGPT-4o and DeepSeek as tools for orthodontic health literacy in public dental education.Scientific reports · 2025Article
- Residual cholesterol inflammatory index and its prognostic role in mortality among individuals with cardiovascular-kidney-metabolic syndrome stages 0-3 based on U.S. and Chinese national cohorts.European journal of medical research · 2025Article
- Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery.Current oncology (Toronto, Ont.) · 2025Review
- Advancements and Applications of Lateral Flow Assays (LFAs): A Comprehensive Review.Sensors (Basel, Switzerland) · 2025Review
- Continual learning across population cohorts with distribution shift: insights from multi-cohort metabolic syndrome identification.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Dual-Stage Clean-Sample Selection for Incremental Noisy Label Learning.Bioengineering (Basel, Switzerland) · 2025Article
- Application of Machine Learning to Breast MR Imaging.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2025Review
- Detecting and Remediating Harmful Data Shifts for the Responsible Deployment of Clinical AI Models.JAMA network open · 2025Article
- CEL: A Continual Learning Model for Disease Outbreak Prediction by Leveraging Domain Adaptation via Elastic Weight Consolidation.Interdisciplinary sciences, computational life sciences · 2025Article
- Anterograde interference in multitask perceptual learning.NPJ science of learning · 2025Article
- Mitigating catastrophic forgetting in Multiple sclerosis lesion segmentation using elastic weight consolidation.NeuroImage. Clinical · 2025Article
- Article
- Relay learning: a physically secure framework for clinical multi-site deep learning.NPJ digital medicine · 2023Article
- A biologically inspired architecture with switching units can learn to generalize across backgrounds.Neural networks : the official journal of the International Neural Network Society · 2023Article
- Artificial intelligence reinforced upconversion nanoparticle-based lateral flow assay via transfer learning.Fundamental research · 2023Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Medical imaging is a central part of clinical diagnosis and treatment guidance. Machine learning has increasingly gained relevance because it captures features of disease and treatment response that are relevant for therapeutic decision-making. In clinical practice, the continuous progress of image acquisition technology or diagnostic procedures, the diversity of scanners, and evolving imaging protocols hamper the utility of machine learning, as prediction accuracy on new data deteriorates, or models become outdated due to these domain shifts. We propose a continual learning approach to deal with such domain shifts occurring at unknown time points. We adapt models to emerging variations in a continuous data stream while counteracting catastrophic forgetting. A dynamic memory enables rehearsal on a subset of diverse training data to mitigate forgetting while enabling models to expand to new domains. The technique balances memory by detecting pseudo-domains, representing different style clusters within the data stream. Evaluation of two different tasks, cardiac segmentation in magnetic resonance imaging and lung nodule detection in computed tomography, demonstrate a consistent advantage of the method.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.